Selecting prospective bidders to whom to promote an online auction based upon bidding history

ABSTRACT

A facility for identifying users to whom to promote a selected auction is described. The facility maintains a representation of bidding histories of a plurality of users. At a point at which certain users have already bid in the selected auction, the facility identifies users that have not already bid in the selected auction that have bidding histories that are similar to those of users that have already bid in the selected auction. The facility then promotes the selected auction to the identified users.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.11/970,380 filed Jan. 7, 2008 entitled “SELECTING PROSPECTIVE BIDDERS TOWHOM TO PROMOTE AN ONLINE AUCTION BASED UPON BIDDING HISTORY,” which isa divisional of U.S. patent application Ser. No. 09/742,273 filed Dec.19, 2000 entitled “SELECTING PROSPECTIVE BIDDERS TO WHOM TO PROMOTE ANONLINE AUCTION BASED UPON BIDDING HISTORY,” which claims the benefit ofU.S. Provisional Application No. 60/171,843 filed Dec. 22, 1999 whichare hereby incorporated by reference in their entirety.

TECHNICAL FIELD

The present invention is directed to the field of electronic commerce,and, more particularly, to the field of online marketing techniques.

BACKGROUND

The auction is a sales technique in which prospective buyers arepermitted to bid on an item offered for sale, and the item is sold tothe bidder submitting the highest bid.

Online auctions have recently emerged, in which auctions are conductedon a World Wide Web site or in a similar environment. Users of computersystems connected to the computer system on which the online auction isconducted can view descriptions and pictures of items offered for sale,as well as bid on items using their computer systems.

Operators of online auctions derive revenue from the online auctions ina variety of ways. For example, an online auction operator may chargesellers a flat sales fee to auction each item, or may charge sellers asales fee measured by a percentage of the sale price. Alternatively, anonline auction operator may sell advertising that is viewed by sellersand/or bidders.

Generally, however, the level of profitability of online auctions totheir operator is directly related to the average number of bidders thatbid in each auction. Where an auction operator charges sellers a salesfee measured by a percentage of the sale price, a larger number ofbidders produces a higher sales price, from which the operator collectsa higher sales fee. Where an auction operator charges sellers a salesfee of any kind, a larger number of bidders produces a higher salesprice, and a larger seller profit, thereby encouraging sellers toinitiate more auctions that produce more sales fees for the operator.Where an auction operator sells advertising on the auction site, alarger number of bidders produces a larger number of advertisingimpressions, and therefore greater advertising revenue.

Accordingly, an effective technique for increasing the number of biddersbidding in an online auction would have significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a high-level block diagram of the computer system upon whichthe facility preferably executes.

FIG. 2 is a flow diagram showing the steps preferably performed by thefacility in order to identify users to whom to promote a selected onlineauction once the auction has begun based upon the identities of theusers that have already bid in other auctions.

FIG. 3 is a flow diagram showing the steps preferably performed by thefacility in order to identify users to whom to promote a selected onlineauction once the auction has begun using clusters of users each havingsimilar bidding histories.

FIG. 4 is a display diagram showing the manner in which the facilitypreferably promotes an auction to identified users.

DETAILED DESCRIPTION

A software facility for selecting prospective bidders to whom to promotean online auction based upon bidding history (“the facility”) isprovided. The facility operates in conjunction with an online auctionsystem that tracks the users bidding on each of a number of onlineauctions.

For an auction selected for promotion, the facility obtains a list ofthe bidders that have bid in the selected auction. The facility usesthis list to identify other auctions, either completed or in progress,in which at least a threshold percentage of the bidders in the selectedauction have also bid. In each identified auction, the facilityidentifies the bidders that have not yet bid on the selected auction.The facility may also use clustering techniques to identify users havingbidding histories that are similar to those of the bidders in theselected auction.

The facility then promotes the selected auction to the identifiedbidders. In some embodiments, the facility sends an email message to theidentified bidders suggesting that they bid in the selected auction. Inother embodiments, the facility displays a suggestion to bid in theselected auction on a web page served to the identified bidders.

In this manner, the facility promotes the selected auction to a group ofbidders that both (1) is likely to be receptive to receiving informationabout the selected auction, and (2) is likely to actually bid in theselected auction, thereby increasing the number of bidders in theselected auction and increasing profitability for the auction operator.

In additional embodiments, the facility is applied to markets other thanonline auctions, such as web merchants that sell items for a fixedprice. Where a large percentage of the purchasers of a first item alsopurchase a second item, the facility preferably promotes the first itemto purchasers of the second item that have not purchased the first item,and vice versa.

Further embodiments of the facility are applied across markets. Forexample, where a large percentage of the users that purchased aparticular item from a web merchant also bid in a particular auction,the facility preferably promotes the auction to the purchasers that havenot yet bid, and the purchase to the bidders that have not yetpurchased.

FIG. 1 is a high-level block diagram of the computer system upon whichthe facility preferably executes. The computer system 100 contains oneor more central processing units (CPUs) 110, input/output devices 120,and a computer memory (memory) 130. Among the input/output devices is apersistent storage device 121, such as a hard disk drive, and acomputer-readable media drive 122, which can be used to install softwareproducts, including components of the facility, which are provided on acomputer-readable medium, such as a CD-ROM. The input/output devicesalso include a network connection 123, through which the computer system100 may by connected to the network to be analyzed by the facility. Thememory 130 preferably contains the prospective bidder identificationfacility 131, as well as a bidding history representation 132 and apromotion history representation 133 both preferably generated and usedby the facility. While items 131-133 are preferably stored in memorywhile being used, those skilled in the art will appreciate that theseitems, or portions of them, may be transferred between memory and thepersistent storage device for purposes of memory management and dataintegrity. While the facility is preferably implemented on a computersystem configured as described above, those skilled in the art willrecognize that it may also be implemented on computer systems havingdifferent configurations, or distributed across multiple computersystems.

To more fully illustrate its implementation and operation, the facilityis described in conjunction with an example.

FIG. 2 is a flow diagram showing the steps preferably performed by thefacility in order to identify users to whom to promote a selected onlineauction once the auction has begun based upon the identities of theusers that have already bid in other auctions. In steps 201-209, thefacility loops through each of a set of auctions other than the selectedauction in which users have bid. In a preferred embodiment, the set ofauctions includes both incomplete and completed auctions. In step 202,if more than a threshold number of users that bid in the selectedauction bid in the current auction, then the facility continues in step203, else the facility continues in step 209. The threshold used by thefacility may be either an absolute threshold expressed in number ofusers, or a relative threshold, expressed as a percentage of the usersbidding in the selected auction. In one embodiment, the facility usesthe relative threshold of seventy percent of the users bidding in theselected auction.

In steps 203-208, the facility loops through each user that bid in thecurrent auction. In step 204, if the user has also already bid in theselected auction, then the facility continues in step 208 to skip theuser, else the facility continues in step 205. In step 205, if theselected auction has already been promoted to the user, then thefacility continues in step 208 to skip the user, else the facilitycontinues in step 206. In step 206, the facility promotes the auction tothe user, since the user bid in a related auction, but has not yet bidin the selected auction or had the selected auction promoted to him orher. The process of promoting the selected auction to a user isdiscussed in greater detail below in conjunction with FIG. 4. In step207, the facility adds the user to the list of users to whom the auctionhas been promoted. In step 208, if additional users remain, then thefacility loops back to step 203 to process the next user. After the loopof steps 203-208 has completed, the facility continues in step 209. Instep 209, if additional auctions in the set remain, then the facilityloops back to step 201 to process the next auction in the set. After theloop of steps 201-209 is completed, these steps conclude.

Tables 1 and 2 below show the result of applying the steps shown in FIG.2 to sample auctions. Table 1 shows the state in which the facilityidentifies users to whom to promote an auction called auction 3.

TABLE 1 auction 1 auction 2 auction 3 auction 3 already bidders biddersbidders promoted to user 1 user 2  user 7  user 5  user 2 user 5  user11 user 22 user 3 user 7  user 12 user 4 user 11 user 13 user 5 user 12user 6 user 19 user 7 user 22

Table 1 shows a list of the users that have bid in auction 1, a list ofthe users that have bid in auction 2, a list of the users that have bidin auction 3, and a list of the users to whom auction 3 has already beenpromoted. In order to identify users to whom to promote auction 3, thefacility examines the lists of users that have bid in auctions 1 and 2.In the example, the facility applies the threshold that, in order foranother auction to be considered, at least seventy percent of the usersthat bid in the selected auction must have bid in the other auction. Interms of the number of users that have bid in auction 3 in the example,this means that at least three of the users that have bid in auction 3must have bid in each of the other two auctions for them to beconsidered (4×70%=2.8).

It can be seen that only one of the users that bid in auction 3 (user 7)also bid in auction 1. Since 1 is less than 3, the facility does not usethe list of users that have bid in auction 1. On the other hand, it canbe seen that three of the users that bid in auction 3 (user 7, user 11,and user 12) also bid in auction 2. Because 3 is at least as large as 3,the list of users that have bid in auction 2 is considered. The facilityconsiders the list of users that have bid in auction 2 as follows: user2 has not bid in auction 3, and auction 3 has not yet been promoted touser 2, so the facility promotes auction 3 to user 2; auction 3 hasalready been promoted to user 5, so the facility does not promoteauction 3 to user 5; user 7 has already bid in auction 3, so thefacility does not promote auction 3 to user 7; user 11 has already bidin auction 3, so the facility does not promote auction 3 to user 11;user 12 has already bid in auction 3, so the facility does not promoteauction 3 to user 12; user 19 has not bid in auction 3, and auction 3has not yet been promoted to user 19, so the facility promotes auction 3to user 19; and auction 3 has already been promoted to user 22, so thefacility does not promote auction 3 to user 22. Accordingly, in applyingthe steps shown in FIG. 2, the facility determines to promote auction 3to the users shown in Table 2 below.

TABLE 2 promote auction 3 to user 2  user 19

In some embodiments, the facility preferably uses clusters of users thatare generated in such a way that the users in each cluster have similarbidding histories. Such clusters are preferably generated usingwell-known clustering techniques, such as those described in Anil K.Jain and Richard C. Dubes, Algorithms for Clustering Data, 1988, pp.629-799; Phipps Arabie et al., Clustering and Classification, 1996;Richard A. Johnson and Dean A. Wichern, Applied Multivariate StatisticalAnalysis, 1998; and/or Leonard Kaufman and Peter J. Rousseau, FindingGroups in Data: An Introduction to Cluster Analysis, 1990.

FIG. 3 is a flow diagram showing the steps preferably performed by thefacility in order to identify users to whom to promote a selected onlineauction once the auction has begun using clusters of users each havingsimilar bidding histories. The facility preferably performs these stepsonce for each bid that is received in the selected auction. In someembodiments, these steps are only performed in response to a user'sfirst bid in the selected auction. The steps may be performed directlyin response to each bid, or may be deferred for later performance in abatch mode. In step 301, the facility receives a bid in the selectedauction from a selected user. In steps 302-310, the facility loopsthrough each of a set of user clusters to which the selected userbelongs. In step 303, if more than a threshold number of users in thecurrent cluster bid in the selected auction, then the facility continuesin step 304, else the facility continues in step 310. The threshold usedby the facility may be either an absolute threshold expressed in numberof users, or a relative threshold, expressed as a percentage of users inthe current cluster. In one embodiment, the facility uses the relativethreshold of fifty percent of the users in the current cluster. In steps304-309, the facility loops through each user in the current cluster. Instep 305, if the current user bid in the selected auction, then thefacility continues in step 309 to skip the user, else the facilitycontinues in step 306. In step 306, if the selected auction has alreadybeen promoted to the current user, then the facility continues in step309 to skip the current user, else the facility continues in step 307.In step 307, the facility promotes the auction to the current user,since the current user is a member of the cluster, but has not yet bidin the selected auction or had the selected auction promoted to him orher. The process of promoting the selected auction to a user isdiscussed in greater detail below in conjunction with FIG. 4. In step308, the facility adds the user to the list of users to whom the auctionhas been promoted. In step 309, if additional users remain in thecluster, then the facility loops back to step 303 to process the nextuser. After the loop of steps 304-309 has completed, the facilitycontinues in step 310. In step 310, if additional clusters remain, thenthe facility loops back to step 302 to process the next cluster. Afterthe loop of steps 302-310 is completed, these steps conclude.

Tables 3 and 4 below show the result of applying the steps shown in FIG.3 to sample auctions and clusters. Table 3 shows the state in which thefacility identifies users to whom to promote auction 3.

TABLE 3 cluster 1 cluster 2 auction 3 auction 3 already members membersbidders promoted to user 7  user 7  user 7  user 5  user 8  user 8  user11 user 22 user 24  user 11 user 12 user 27  user 12 user 13 user 99 user 13 user 101 user 19 user 22

Table 3 shows a list of the users in cluster 1, a list of the users incluster 2, a list of the users that have been in auction 3, and a listof the users to whom auction 3 has already been promoted. In order toidentify users to whom to promote auction 3, the facility examines thelists of users that are members of clusters 1 and 2. In the example, thefacility applies the threshold that, in order for a cluster to beconsidered, at least fifty percent of the users in the cluster must havebid in the selected auction. It can be seen that only one of the six incluster 1 (user 7) is among the four users that have bid in auction 3.Because seventeen percent (1 user÷6 users=17%) is less than fiftypercent, the facility does not use the list of users in cluster 1. Onthe other hand, it can been seem that four of the seven members ofcluster 2 (user 7, user 11, user 12, and user 13) have bid in auction 3.Because fifty-seven percent (4 user÷ 7 users=57%) is greater than fiftypercent, the list of users in cluster 2 is considered. The facilityconsiders the list of users in cluster 2 as follows: user 7 has alreadybid in auction 3, so the facility does not promote auction 3 to user 7.User 8 has not bid in auction 3, and auction 3 has not yet been promotedto user 8, so the facility promotes auction 3 to user 8; user 11 hasalready bid in auction 3, so the facility does not promote auction 3 touser 11; user 12 has already bid in auction 3, so the facility does notpromote auction 3 to user 12; user 13 has already bid in auction 3, sothe facility does not promote auction 3 to user 13; user 19 has not bidin auction 3, and auction 3 has not yet been promoted to user 19, so thefacility promotes auction 3 to user 19; and auction 3 has already beenpromoted to user 22, so the facility does not promote auction 3 to user22. Accordingly, in applying the steps shown in FIG. 3, the facilitydetermines to promote auction 3 to the user shown in Table 4 below.

TABLE 4 promote auction 3 to user 8  user 19

FIG. 4 is a display diagram showing the manner in which the facilitypreferably promotes an auction to identified users. FIG. 4 shows that apromotion 410 is displayed on a display 400. The promotion maypreferably be sent to a user an email message, as an ICQ instantmessage, or as another type of message. The promotion may further bepresented to the user as a web page, either when the user visits a website for the operator of the facility, or when the user visits another,associated web site. The promotion may also be provided in audio form,such as in an automatically initiated telephone call or an automaticallydelivered voicemail message. Those skilled in the art will appreciatethat there are additional media through which the facility can alsoprovide promotions to selected users.

The promotion 410 invites the user to consider bidding on the selectedauction. The promotion includes information 411 about the auction, suchas a description of the item offered for sale, the time at which theauction opened, the time at which the auction will close, and thecurrent bid amount. Those skilled in the art will appreciate that otherinformation about the selected auction could also be included. Thepromotion preferably further includes a visual control 412, such as abutton, that the user may operate in order to open the web page used byusers to bid in the selected auction.

It will be understood by those skilled in the art that theabove-described facility could be adapted or extended in various ways.While the foregoing description makes reference to preferredembodiments, the scope of the invention is defined solely by the claimsthat follow and the elements recited therein.

1. A method in a computer system for identifying users to whom topromote a selected auction among a set of users of an auction system,where a proper subset of the clustered users have already bid in theselected auction, the method comprising: identifying, in the computersystem, a plurality of clusters of users among the set of users byapplying at least one predefined clustering technique based at least inpart on user bidding history in the auction system; and in response to abid by one of the set of users in the selected auction, identifying anycluster of which the one of the set of users is a member, a number ofwhose members who have bid in the selected auction being more than apredefined threshold number, and identifying users of the identifiedclusters who have not bid in the selected auction.
 2. The method ofclaim 1, further comprising determining to which of the users of theidentified clusters who have not bid in the selected auction theselected auction has already been promoted, and wherein the identifyingusers of the identified clusters who have not bid in the selectedauction further identifies users of the identified clusters to whom theselected auction has not already been promoted.
 3. The method of claim1, further comprising promoting the selected auction to the identifiedusers.
 4. The method of claim 1, further comprising transmittingelectronic mail messages to the identified users promoting the selectedauction to them.
 5. The method of claim 1, further comprising, when anyof the identified users request a selected web page: incorporating inthe selected web page information promoting the selected auction; andafter such incorporation, returning the selected web page to the user.6. A non-transitory computer-readable medium whose contents cause acomputer system to identify users to whom to promote a selected auctionamong a set of users of an auction system, where a proper subset of theclustered users have already bid in the selected auction, by:identifying a cluster of users among the set of users by applying atleast one predefined clustering technique based at least in part on userbidding history in the auction system; and in response to a bid in theselected auction by a selected user that is a member of the cluster,identifying users of the cluster who have not bid in the selectedauction.
 7. The non-transitory computer-readable medium of claim 6wherein the contents of the non-transitory computer-readable mediumfurther cause the computer system to determine to which of the users ofthe identified clusters who have not bid in the selected auction theselected auction has already been promoted, and wherein the identifyingusers of the cluster who have not bid in the selected auction furtheridentifies users of the identified clusters to whom the selected auctionhas not already been promoted.
 8. The non-transitory computer-readablemedium of claim 6 wherein the contents of the non-transitorycomputer-readable medium further cause the computer system to promotethe selected auction to the identified users.
 9. The non-transitorycomputer-readable medium of claim 6 wherein the contents of thenon-transitory computer-readable medium further cause the computersystem to transmit electronic mail messages to the identified userspromoting the selected auction to them.
 10. The non-transitorycomputer-readable medium of claim 6 wherein the contents of thenon-transitory computer-readable medium further cause the computersystem to, when any of the identified users request a selected web page:incorporate in the selected web page information promoting the selectedauction; and after such incorporation, return the selected web page tothe user.
 11. A system, comprising: at least one computing device; and auser identification application executable in the at least one computingdevice, the user identification application comprising: logic thatidentifies a plurality of clusters of users from a set of users of anauction system by applying at least one predefined clustering techniquebased at least in part on a corresponding bidding history of each one ofthe set of users in the auction system; logic that determines a set ofthe clusters of users, wherein, for each one of the set of the clusters,a number of the corresponding users who have bid in a selected auctionof the auction system meets a predefined threshold number; and logicthat identifies a set of users who have not bid in the selected auctionfrom the set of the clusters of users.
 12. The system of claim 11,wherein each bidding history describes a list of auctions in which therespective user has placed a bid through the auction system.
 13. Thesystem of claim 12, wherein the list of auctions includes at least oneauction that is in progress.
 14. The system of claim 12, wherein thelist of auctions includes at least one auction that is completed. 15.The system of claim 11, wherein the selected auction is selected basedat least in part on a bid in the selected auction by a selected user,and the selected user is a member of each one of the set of clusters.16. The system of claim 11, further comprising: an auction promotionapplication executable in the at least one computing device, the auctionpromotion application comprising: logic that sends a notification thatpromotes the selected auction to at least one of the set of users whohave not bid in the selected auction.
 17. The system of claim 16,wherein the notification is sent to all of the set of users who have notbid in the selected auction.
 18. The system of claim 16, wherein thenotification includes a link to a network page that is configured tofacilitate bidding in the selected auction.
 19. The system of claim 16,wherein the notification includes a description of an item and a currentbid amount.
 20. The system of claim 16, wherein the notification isprovided in an audio form.